Logging training
Running DummyClassifier()
accuracy: 0.732 average_precision: 0.268 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.422
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.732 average_precision: 0.268 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.422

Running GaussianNB()
accuracy: 0.466 average_precision: 0.361 roc_auc: 0.619 recall_macro: 0.570 f1_macro: 0.464
=== new best GaussianNB() (using recall_macro):
accuracy: 0.466 average_precision: 0.361 roc_auc: 0.619 recall_macro: 0.570 f1_macro: 0.464

Running MultinomialNB()
accuracy: 0.732 average_precision: 0.377 roc_auc: 0.614 recall_macro: 0.500 f1_macro: 0.422
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.699 average_precision: 0.305 roc_auc: 0.561 recall_macro: 0.561 f1_macro: 0.562
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.630 average_precision: 0.347 roc_auc: 0.579 recall_macro: 0.564 f1_macro: 0.550
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.699 average_precision: 0.305 roc_auc: 0.561 recall_macro: 0.561 f1_macro: 0.562
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.612 average_precision: 0.406 roc_auc: 0.636 recall_macro: 0.594 f1_macro: 0.570
=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.612 average_precision: 0.406 roc_auc: 0.636 recall_macro: 0.594 f1_macro: 0.570

Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
accuracy: 0.600 average_precision: 0.404 roc_auc: 0.635 recall_macro: 0.592 f1_macro: 0.563

Best model:
LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.612 average_precision: 0.406 roc_auc: 0.636 recall_macro: 0.594 f1_macro: 0.570